“The Algorithm That Runs the World” by Erwin Gianchandani.
From the post:
New Scientist published a great story last week describing the history and evolution of the simplex algorithm — complete with a table capturing “2000 years of algorithms”:
The simplex algorithm directs wares to their destinations the world over [image courtesy PlainPicture/Gozooma via New Scientist].Its services are called upon thousands of times a second to ensure the world’s business runs smoothly — but are its mathematics as dependable as we thought?
YOU MIGHT not have heard of the algorithm that runs the world. Few people have, though it can determine much that goes on in our day-to-day lives: the food we have to eat, our schedule at work, when the train will come to take us there. Somewhere, in some server basement right now, it is probably working on some aspect of your life tomorrow, next week, in a year’s time.
Perhaps ignorance of the algorithm’s workings is bliss. The door to Plato’s Academy in ancient Athens is said to have borne the legend “let no one ignorant of geometry enter”. That was easy enough to say back then, when geometry was firmly grounded in the three dimensions of space our brains were built to cope with. But the algorithm operates in altogether higher planes. Four, five, thousands or even many millions of dimensions: these are the unimaginable spaces the algorithm’s series of mathematical instructions was devised to probe.
Perhaps, though, we should try a little harder to get our heads round it. Because powerful though it undoubtedly is, the algorithm is running into a spot of bother. Its mathematical underpinnings, though not yet structurally unsound, are beginning to crumble at the edges. With so much resting on it, the algorithm may not be quite as dependable as it once seemed [more following the link].
…
A fund manager might similarly want to arrange a portfolio optimally to balance risk and expected return over a range of stocks; a railway timetabler to decide how best to roster staff and trains; or a factory or hospital manager to work out how to juggle finite machine resources or ward space. Each such problem can be depicted as a geometrical shape whose number of dimensions is the number of variables in the problem, and whose boundaries are delineated by whatever constraints there are (see diagram). In each case, we need to box our way through this polytope towards its optimal point.
This is the job of the algorithm.
Its full name is the simplex algorithm, and it emerged in the late 1940s from the work of the US mathematician George Dantzig, who had spent the second world war investigating ways to increase the logistical efficiency of the U.S. air force. Dantzig was a pioneer in the field of what he called linear programming, which uses the mathematics of multidimensional polytopes to solve optimisation problems. One of the first insights he arrived at was that the optimum value of the “target function” — the thing we want to maximise or minimise, be that profit, travelling time or whatever — is guaranteed to lie at one of the corners of the polytope. This instantly makes things much more tractable: there are infinitely many points within any polytope, but only ever a finite number of corners.
If we have just a few dimensions and constraints to play with, this fact is all we need. We can feel our way along the edges of the polytope, testing the value of the target function at every corner until we find its sweet spot. But things rapidly escalate. Even just a 10-dimensional problem with 50 constraints — perhaps trying to assign a schedule of work to 10 people with different expertise and time constraints — may already land us with several billion corners to try out.
Apologies but I saw this article too late to post within the “free” days allowed by New Scientist.
But, I think from Erwin’s post and long quote from the original article, you can see how the simplex algorithm may be very useful where identity is defined in multidimensional space.
The literature in this area is vast and it may not offer an appropriate test for all questions of subject identity.
For example, the possessor of a credit card is presumed to be the owner of the card. Other assumptions are possible, but fraud costs are recouped from fees paid by customers. Creating a lack of interest in more stringent identity tests.
On the other hand, if your situation requires multidimensional identity measures, this may be a useful approach.
PS: Be aware that naming confusion, the sort that can be managed (not solved) by topic maps abounds even in mathematics:
The elements of a polytope are its vertices, edges, faces, cells and so on. The terminology for these is not entirely consistent across different authors. To give just a few examples: Some authors use face to refer to an (n−1)-dimensional element while others use face to denote a 2-face specifically, and others use j-face or k-face to indicate an element of j or k dimensions. Some sources use edge to refer to a ridge, while H. S. M. Coxeter uses cell to denote an (n−1)-dimensional element. (Polytope)